Reduced complexity multi-mode neural network filtering of video data

ABSTRACT

An example device for filtering video data includes a memory configured to store video data; and a processing system comprising one or more processors implemented in circuitry, the processing system being configured to: apply one or more neural network processing blocks to intermediate filtered video data, each of the neural network processing blocks including a first 1×1 convolutional filter, a parametric rectified linear unit (PReLU) filter, a second 1×1 convolutional filter, and a 3×3 convolutional filter; apply additional neural network processing blocks to output of the one or more neural network processing blocks to form filtered video data; and output the filtered video data.

This application claims the benefit of U.S. Provisional Application No.63/367,712, filed Jul. 5, 2022, the entire contents of which areincorporated by reference.

TECHNICAL FIELD

This disclosure relates to video coding, including video encoding andvideo decoding.

BACKGROUND

Digital video capabilities can be incorporated into a wide range ofdevices, including digital televisions, digital direct broadcastsystems, wireless broadcast systems, personal digital assistants (PDAs),laptop or desktop computers, tablet computers, e-book readers, digitalcameras, digital recording devices, digital media players, video gamingdevices, video game consoles, cellular or satellite radio telephones,so-called “smart phones,” video teleconferencing devices, videostreaming devices, and the like. Digital video devices implement videocoding techniques, such as those described in the standards defined byMPEG-2, MPEG-4, ITU-T H.263, ITU-T H.264/MPEG-4, Part 10, Advanced VideoCoding (AVC), ITU-T H.265/High Efficiency Video Coding (HEVC), ITU-TH.266/Versatile Video Coding (VVC), and extensions of such standards, aswell as proprietary video codecs/formats such as AOMedia Video 1 (AV1)developed by the Alliance for Open Media. The video devices maytransmit, receive, encode, decode, and/or store digital videoinformation more efficiently by implementing such video codingtechniques.

Video coding techniques include spatial (intra-picture) predictionand/or temporal (inter-picture) prediction to reduce or removeredundancy inherent in video sequences. For block-based video coding, avideo slice (e.g., a video picture or a portion of a video picture) maybe partitioned into video blocks, which may also be referred to ascoding tree units (CTUs), coding units (CUs) and/or coding nodes. Videoblocks in an intra-coded (I) slice of a picture are encoded usingspatial prediction with respect to reference samples in neighboringblocks in the same picture. Video blocks in an inter-coded (P or B)slice of a picture may use spatial prediction with respect to referencesamples in neighboring blocks in the same picture or temporal predictionwith respect to reference samples in other reference pictures. Picturesmay be referred to as frames, and reference pictures may be referred toas reference frames.

SUMMARY

In general, this disclosure describes techniques for filtering decodedvideo data. That is, this disclosure describes filtering processtechniques to filter a distorted picture. The filtering process may bebased on neural network technologies. The filter process may be used inthe context of advanced video codecs, such as extensions of ITU-TH.266/Versatile Video Coding (VVC) or other video coding standards andvideo codecs.

In one example, a method of filtering video data includes applying oneor more neural network processing blocks to intermediate filtered videodata, each of the neural network processing blocks including a first 1×1convolutional filter, a parametric rectified linear unit (PReLU) filter,a second 1×1 convolutional filter, and a 3×3 convolutional filter;applying additional neural network processing blocks to output of theone or more neural network processing blocks to form filtered videodata; and outputting the filtered video data.

In another example, a device for filtering video data includes a memoryconfigured to store video data; and a processing system comprising oneor more processors implemented in circuitry, the processing system beingconfigured to: apply one or more neural network processing blocks tointermediate filtered video data, each of the neural network processingblocks including a first 1×1 convolutional filter, a parametricrectified linear unit (PReLU) filter, a second 1×1 convolutional filter,and a 3×3 convolutional filter; apply additional neural networkprocessing blocks to output of the one or more neural network processingblocks to form filtered video data; and output the filtered video data.

In another example, a computer-readable storage medium has storedthereon instructions that, when executed, cause a processing system to:apply one or more neural network processing blocks to intermediatefiltered video data, each of the neural network processing blocksincluding a first 1×1 convolutional filter, a parametric rectifiedlinear unit (PReLU) filter, a second 1×1 convolutional filter, and a 3×3convolutional filter; apply additional neural network processing blocksto output of the one or more neural network processing blocks to formfiltered video data; and output the filtered video data.

In another example, a device for filtering video data includes means forapplying one or more neural network processing blocks to intermediatefiltered video data, each of the neural network processing blocksincluding a first 1×1 convolutional filter, a parametric rectifiedlinear unit (PReLU) filter, a second 1×1 convolutional filter, and a 3×3convolutional filter; means for applying additional neural networkprocessing blocks to output of the one or more neural network processingblocks to form filtered video data; and means for outputting thefiltered video data.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description, drawings, and claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example video encoding anddecoding system that may perform the techniques of this disclosure.

FIG. 2 is a conceptual diagram illustrating a hybrid video codingframework.

FIG. 3 is a conceptual diagram illustrating a hierarchical predictionstructure using a group of pictures (GOP) size of 16.

FIG. 4 is a conceptual diagram illustrating a neural network basedfilter with four layers.

FIG. 5 is a conceptual diagram illustrating an example neural network(NN)-based filtering solution with multiple modes.

FIG. 6 is a conceptual diagram illustrating an example attentionresidual block of the neural network filter of FIG. 5 .

FIG. 7 is a conceptual diagram illustrating an example spatial attentionlayer of FIG. 5 .

FIG. 8 is a conceptual diagram illustrating an example residual blockstructure that may be substituted for the attention residual block ofFIG. 5 according to the techniques of this disclosure.

FIG. 9 is a conceptual diagram illustrating an example residual blockstructure of the example of FIG. 8 .

FIG. 10 is a conceptual diagram illustrating an example filtering blockstructure that may be substituted for the attention residual block ofFIG. 5 according to the techniques of this disclosure.

FIG. 11 is a conceptual diagram illustrating an example filter blockstructure of the example of FIG. 10 .

FIG. 12 is a block diagram illustrating an example video encoder thatmay perform the techniques of this disclosure.

FIG. 13 is a block diagram illustrating an example video decoder thatmay perform the techniques of this disclosure.

FIG. 14 is a flowchart illustrating an example method for encoding acurrent block in accordance with the techniques of this disclosure.

FIG. 15 is a flowchart illustrating an example method for decoding acurrent block in accordance with the techniques of this disclosure.

FIG. 16 is a flowchart illustrating an example method for filteringvideo data using a series of one or more residual blocks of a CNN filterin accordance with the techniques of this disclosure.

FIG. 17 is a flowchart illustrating an example method for executing aresidual block of a CNN filter according to the techniques of thisdisclosure.

DETAILED DESCRIPTION

Video encoding is typically a lossy procedure. For example, during thevideo encoding process, blocks of video data may be encoded usingquantization and transformation. In general, quantization of valuesinvolves reducing a number of least significant bits for the values,which generally is not recoverable. In general, such reduction of bitsis performed in a manner so as to avoid detectability of the loss.However, at times, such losses may lead to detectable artifacts in thevideo data, such as blockiness artifacts.

Filtering may be applied to decoded and/or reconstructed video data toenhance the video data, which may improve the output video data. Forexample, filtering may compensate for the blockiness artifacts or otherlosses in the video data. Studies have shown that neural network (NN)based filtering techniques are highly capable of improving decodedand/or reproduced video data. NN-based filtering techniques can behighly complex and require significant processing power to performeffectively.

This disclosure describes simplifications that may be applied toNN-based filtering techniques. Application of these simplifications mayreduce the processing performed by one or more processors to performNN-based filtering. In this manner, the techniques of this disclosuremay improve the performance of a video coding device. Likewise, thesetechniques may enable many more devices to perform NN-based filtering,thereby improving the field of video coding generally.

FIG. 1 is a block diagram illustrating an example video encoding anddecoding system 100 that may perform the techniques of this disclosure.The techniques of this disclosure are generally directed to coding(encoding and/or decoding) video data. In general, video data includesany data for processing a video. Thus, video data may include raw,uncoded video, encoded video, decoded (e.g., reconstructed) video, andvideo metadata, such as signaling data.

As shown in FIG. 1 , system 100 includes a source device 102 thatprovides encoded video data to be decoded and displayed by a destinationdevice 116, in this example. In particular, source device 102 providesthe video data to destination device 116 via a computer-readable medium110. Source device 102 and destination device 116 may comprise any of awide range of devices, including desktop computers, notebook (i.e.,laptop) computers, mobile devices, tablet computers, set-top boxes,telephone handsets such as smartphones, televisions, cameras, displaydevices, digital media players, video gaming consoles, video streamingdevice, broadcast receiver devices, or the like. In some cases, sourcedevice 102 and destination device 116 may be equipped for wirelesscommunication, and thus may be referred to as wireless communicationdevices.

In the example of FIG. 1 , source device 102 includes video source 104,memory 106, video encoder 200, and output interface 108. Destinationdevice 116 includes input interface 122, video decoder 300, memory 120,and display device 118. In accordance with this disclosure, videoencoder 200 of source device 102 and video decoder 300 of destinationdevice 116 may be configured to apply the techniques for neural networkbased filtering. Thus, source device 102 represents an example of avideo encoding device, while destination device 116 represents anexample of a video decoding device. In other examples, a source deviceand a destination device may include other components or arrangements.For example, source device 102 may receive video data from an externalvideo source, such as an external camera. Likewise, destination device116 may interface with an external display device, rather than includean integrated display device.

System 100 as shown in FIG. 1 is merely one example. In general, anydigital video encoding and/or decoding device may perform techniques forneural network based filtering. Source device 102 and destination device116 are merely examples of such coding devices in which source device102 generates coded video data for transmission to destination device116. This disclosure refers to a “coding” device as a device thatperforms coding (encoding and/or decoding) of data. Thus, video encoder200 and video decoder 300 represent examples of coding devices, inparticular, a video encoder and a video decoder, respectively. In someexamples, source device 102 and destination device 116 may operate in asubstantially symmetrical manner such that each of source device 102 anddestination device 116 includes video encoding and decoding components.Hence, system 100 may support one-way or two-way video transmissionbetween source device 102 and destination device 116, e.g., for videostreaming, video playback, video broadcasting, or video telephony.

In general, video source 104 represents a source of video data (i.e.,raw, uncoded video data) and provides a sequential series of pictures(also referred to as “frames”) of the video data to video encoder 200,which encodes data for the pictures. Video source 104 of source device102 may include a video capture device, such as a video camera, a videoarchive containing previously captured raw video, and/or a video feedinterface to receive video from a video content provider. As a furtheralternative, video source 104 may generate computer graphics-based dataas the source video, or a combination of live video, archived video, andcomputer-generated video. In each case, video encoder 200 encodes thecaptured, pre-captured, or computer-generated video data. Video encoder200 may rearrange the pictures from the received order (sometimesreferred to as “display order”) into a coding order for coding. Videoencoder 200 may generate a bitstream including encoded video data.Source device 102 may then output the encoded video data via outputinterface 108 onto computer-readable medium 110 for reception and/orretrieval by, e.g., input interface 122 of destination device 116.

Memory 106 of source device 102 and memory 120 of destination device 116represent general purpose memories. In some examples, memories 106, 120may store raw video data, e.g., raw video from video source 104 and raw,decoded video data from video decoder 300. Additionally oralternatively, memories 106, 120 may store software instructionsexecutable by, e.g., video encoder 200 and video decoder 300,respectively. Although memory 106 and memory 120 are shown separatelyfrom video encoder 200 and video decoder 300 in this example, it shouldbe understood that video encoder 200 and video decoder 300 may alsoinclude internal memories for functionally similar or equivalentpurposes. Furthermore, memories 106, 120 may store encoded video data,e.g., output from video encoder 200 and input to video decoder 300. Insome examples, portions of memories 106, 120 may be allocated as one ormore video buffers, e.g., to store raw, decoded, and/or encoded videodata.

Computer-readable medium 110 may represent any type of medium or devicecapable of transporting the encoded video data from source device 102 todestination device 116. In one example, computer-readable medium 110represents a communication medium to enable source device 102 totransmit encoded video data directly to destination device 116 inreal-time, e.g., via a radio frequency network or computer-basednetwork. Output interface 108 may modulate a transmission signalincluding the encoded video data, and input interface 122 may demodulatethe received transmission signal, according to a communication standard,such as a wireless communication protocol. The communication medium maycomprise any wireless or wired communication medium, such as a radiofrequency (RF) spectrum or one or more physical transmission lines. Thecommunication medium may form part of a packet-based network, such as alocal area network, a wide-area network, or a global network such as theInternet. The communication medium may include routers, switches, basestations, or any other equipment that may be useful to facilitatecommunication from source device 102 to destination device 116.

In some examples, source device 102 may output encoded data from outputinterface 108 to storage device 112. Similarly, destination device 116may access encoded data from storage device 112 via input interface 122.Storage device 112 may include any of a variety of distributed orlocally accessed data storage media such as a hard drive, Blu-ray discs,DVDs, CD-ROMs, flash memory, volatile or non-volatile memory, or anyother suitable digital storage media for storing encoded video data.

In some examples, source device 102 may output encoded video data tofile server 114 or another intermediate storage device that may storethe encoded video data generated by source device 102. Destinationdevice 116 may access stored video data from file server 114 viastreaming or download.

File server 114 may be any type of server device capable of storingencoded video data and transmitting that encoded video data to thedestination device 116. File server 114 may represent a web server(e.g., for a website), a server configured to provide a file transferprotocol service (such as File Transfer Protocol (FTP) or File Deliveryover Unidirectional Transport (FLUTE) protocol), a content deliverynetwork (CDN) device, a hypertext transfer protocol (HTTP) server, aMultimedia Broadcast Multicast Service (MBMS) or Enhanced MBMS (eMBMS)server, and/or a network attached storage (NAS) device. File server 114may, additionally or alternatively, implement one or more HTTP streamingprotocols, such as Dynamic Adaptive Streaming over HTTP (DASH), HTTPLive Streaming (HLS), Real Time Streaming Protocol (RTSP), HTTP DynamicStreaming, or the like.

Destination device 116 may access encoded video data from file server114 through any standard data connection, including an Internetconnection. This may include a wireless channel (e.g., a Wi-Ficonnection), a wired connection (e.g., digital subscriber line (DSL),cable modem, etc.), or a combination of both that is suitable foraccessing encoded video data stored on file server 114. Input interface122 may be configured to operate according to any one or more of thevarious protocols discussed above for retrieving or receiving media datafrom file server 114, or other such protocols for retrieving media data.

Output interface 108 and input interface 122 may represent wirelesstransmitters/receivers, modems, wired networking components (e.g.,Ethernet cards), wireless communication components that operateaccording to any of a variety of IEEE 802.11 standards, or otherphysical components. In examples where output interface 108 and inputinterface 122 comprise wireless components, output interface 108 andinput interface 122 may be configured to transfer data, such as encodedvideo data, according to a cellular communication standard, such as 4G,4G-LTE (Long-Term Evolution), LTE Advanced, 5G, or the like. In someexamples where output interface 108 comprises a wireless transmitter,output interface 108 and input interface 122 may be configured totransfer data, such as encoded video data, according to other wirelessstandards, such as an IEEE 802.11 specification, an IEEE 802.15specification (e.g., ZigBee™), a Bluetooth™ standard, or the like. Insome examples, source device 102 and/or destination device 116 mayinclude respective system-on-a-chip (SoC) devices. For example, sourcedevice 102 may include an SoC device to perform the functionalityattributed to video encoder 200 and/or output interface 108, anddestination device 116 may include an SoC device to perform thefunctionality attributed to video decoder 300 and/or input interface122.

The techniques of this disclosure may be applied to video coding insupport of any of a variety of multimedia applications, such asover-the-air television broadcasts, cable television transmissions,satellite television transmissions, Internet streaming videotransmissions, such as dynamic adaptive streaming over HTTP (DASH),digital video that is encoded onto a data storage medium, decoding ofdigital video stored on a data storage medium, or other applications.

Input interface 122 of destination device 116 receives an encoded videobitstream from computer-readable medium 110 (e.g., a communicationmedium, storage device 112, file server 114, or the like). The encodedvideo bitstream may include signaling information defined by videoencoder 200, which is also used by video decoder 300, such as syntaxelements having values that describe characteristics and/or processingof video blocks or other coded units (e.g., slices, pictures, groups ofpictures, sequences, or the like). Display device 118 displays decodedpictures of the decoded video data to a user. Display device 118 mayrepresent any of a variety of display devices such as a liquid crystaldisplay (LCD), a plasma display, an organic light emitting diode (OLED)display, or another type of display device.

Although not shown in FIG. 1 , in some examples, video encoder 200 andvideo decoder 300 may each be integrated with an audio encoder and/oraudio decoder, and may include appropriate MUX-DEMUX units, or otherhardware and/or software, to handle multiplexed streams including bothaudio and video in a common data stream.

Video encoder 200 and video decoder 300 each may be implemented as anyof a variety of suitable encoder and/or decoder circuitry, such as oneor more microprocessors, digital signal processors (DSPs), applicationspecific integrated circuits (ASICs), field programmable gate arrays(FPGAs), discrete logic, software, hardware, firmware or anycombinations thereof. When the techniques are implemented partially insoftware, a device may store instructions for the software in asuitable, non-transitory computer-readable medium and execute theinstructions in hardware using one or more processors to perform thetechniques of this disclosure. Each of video encoder 200 and videodecoder 300 may be included in one or more encoders or decoders, eitherof which may be integrated as part of a combined encoder/decoder (CODEC)in a respective device. A device including video encoder 200 and/orvideo decoder 300 may comprise an integrated circuit, a microprocessor,and/or a wireless communication device, such as a cellular telephone.

Video encoder 200 and video decoder 300 may operate according to a videocoding standard, such as ITU-T H.265, also referred to as HighEfficiency Video Coding (HEVC) or extensions thereto, such as themulti-view and/or scalable video coding extensions. Alternatively, videoencoder 200 and video decoder 300 may operate according to otherproprietary or industry standards, such as ITU-T H.266, also referred toas Versatile Video Coding (VVC). In other examples, video encoder 200and video decoder 300 may operate according to a proprietary videocodec/format, such as AOMedia Video 1 (AV1), extensions of AV1, and/orsuccessor versions of AV1 (e.g., AV2). In other examples, video encoder200 and video decoder 300 may operate according to other proprietaryformats or industry standards. The techniques of this disclosure,however, are not limited to any particular coding standard or format. Ingeneral, video encoder 200 and video decoder 300 may be configured toperform the techniques of this disclosure in conjunction with any videocoding techniques that use neural network based filtering.

In general, video encoder 200 and video decoder 300 may performblock-based coding of pictures. The term “block” generally refers to astructure including data to be processed (e.g., encoded, decoded, orotherwise used in the encoding and/or decoding process). For example, ablock may include a two-dimensional matrix of samples of luminanceand/or chrominance data. In general, video encoder 200 and video decoder300 may code video data represented in a YUV (e.g., Y, Cb, Cr) format.That is, rather than coding red, green, and blue (RGB) data for samplesof a picture, video encoder 200 and video decoder 300 may code luminanceand chrominance components, where the chrominance components may includeboth red hue and blue hue chrominance components. In some examples,video encoder 200 converts received RGB formatted data to a YUVrepresentation prior to encoding, and video decoder 300 converts the YUVrepresentation to the RGB format. Alternatively, pre- andpost-processing units (not shown) may perform these conversions.

This disclosure may generally refer to coding (e.g., encoding anddecoding) of pictures to include the process of encoding or decodingdata of the picture. Similarly, this disclosure may refer to coding ofblocks of a picture to include the process of encoding or decoding datafor the blocks, e.g., prediction and/or residual coding. An encodedvideo bitstream generally includes a series of values for syntaxelements representative of coding decisions (e.g., coding modes) andpartitioning of pictures into blocks. Thus, references to coding apicture or a block should generally be understood as coding values forsyntax elements forming the picture or block.

HEVC defines various blocks, including coding units (CUs), predictionunits (PUs), and transform units (TUs). According to HEVC, a video coder(such as video encoder 200) partitions a coding tree unit (CTU) into CUsaccording to a quadtree structure. That is, the video coder partitionsCTUs and CUs into four equal, non-overlapping squares, and each node ofthe quadtree has either zero or four child nodes. Nodes without childnodes may be referred to as “leaf nodes,” and CUs of such leaf nodes mayinclude one or more PUs and/or one or more TUs. The video coder mayfurther partition PUs and TUs. For example, in HEVC, a residual quadtree(RQT) represents partitioning of TUs. In HEVC, PUs representinter-prediction data, while TUs represent residual data. CUs that areintra-predicted include intra-prediction information, such as anintra-mode indication.

As another example, video encoder 200 and video decoder 300 may beconfigured to operate according to VVC. According to VVC, a video coder(such as video encoder 200) partitions a picture into a plurality ofcoding tree units (CTUs). Video encoder 200 may partition a CTUaccording to a tree structure, such as a quadtree-binary tree (QTBT)structure or Multi-Type Tree (MTT) structure. The QTBT structure removesthe concepts of multiple partition types, such as the separation betweenCUs, PUs, and TUs of HEVC. A QTBT structure includes two levels: a firstlevel partitioned according to quadtree partitioning, and a second levelpartitioned according to binary tree partitioning. A root node of theQTBT structure corresponds to a CTU. Leaf nodes of the binary treescorrespond to coding units (CUs).

In an MTT partitioning structure, blocks may be partitioned using aquadtree (QT) partition, a binary tree (BT) partition, and one or moretypes of triple tree (TT) (also called ternary tree (TT)) partitions. Atriple or ternary tree partition is a partition where a block is splitinto three sub-blocks. In some examples, a triple or ternary treepartition divides a block into three sub-blocks without dividing theoriginal block through the center. The partitioning types in MTT (e.g.,QT, BT, and TT), may be symmetrical or asymmetrical.

When operating according to the AV1 codec, video encoder 200 and videodecoder 300 may be configured to code video data in blocks. In AV1, thelargest coding block that can be processed is called a superblock. InAV1, a superblock can be either 128×128 luma samples or 64×64 lumasamples. However, in successor video coding formats (e.g., AV2), asuperblock may be defined by different (e.g., larger) luma sample sizes.In some examples, a superblock is the top level of a block quadtree.Video encoder 200 may further partition a superblock into smaller codingblocks. Video encoder 200 may partition a superblock and other codingblocks into smaller blocks using square or non-square partitioning.Non-square blocks may include N/2×N, N×N/2, N/4×N, and N×N/4 blocks.Video encoder 200 and video decoder 300 may perform separate predictionand transform processes on each of the coding blocks.

AV1 also defines a tile of video data. A tile is a rectangular array ofsuperblocks that may be coded independently of other tiles. That is,video encoder 200 and video decoder 300 may encode and decode,respectively, coding blocks within a tile without using video data fromother tiles. However, video encoder 200 and video decoder 300 mayperform filtering across tile boundaries. Tiles may be uniform ornon-uniform in size. Tile-based coding may enable parallel processingand/or multi-threading for encoder and decoder implementations.

In some examples, video encoder 200 and video decoder 300 may use asingle QTBT or MTT structure to represent each of the luminance andchrominance components, while in other examples, video encoder 200 andvideo decoder 300 may use two or more QTBT or MTT structures, such asone QTBT/MTT structure for the luminance component and another QTBT/MTTstructure for both chrominance components (or two QTBT/MTT structuresfor respective chrominance components).

Video encoder 200 and video decoder 300 may be configured to usequadtree partitioning, QTBT partitioning, MTT partitioning, superblockpartitioning, or other partitioning structures.

In some examples, a CTU includes a coding tree block (CTB) of lumasamples, two corresponding CTBs of chroma samples of a picture that hasthree sample arrays, or a CTB of samples of a monochrome picture or apicture that is coded using three separate color planes and syntaxstructures used to code the samples. A CTB may be an N×N block ofsamples for some value of N such that the division of a component intoCTBs is a partitioning. A component may be an array or single samplefrom one of the three arrays (luma and two chroma) for a picture in4:2:0, 4:2:2, or 4:4:4 color format, or an array or a single sample ofthe array for a picture in monochrome format. In some examples, a codingblock is an M×N block of samples for some values of M and N such that adivision of a CTB into coding blocks is a partitioning.

The blocks (e.g., CTUs or CUs) may be grouped in various ways in apicture. As one example, a brick may refer to a rectangular region ofCTU rows within a particular tile in a picture. A tile may be arectangular region of CTUs within a particular tile column and aparticular tile row in a picture. A tile column refers to a rectangularregion of CTUs having a height equal to the height of the picture and awidth specified by syntax elements (e.g., such as in a picture parameterset). A tile row refers to a rectangular region of CTUs having a heightspecified by syntax elements (e.g., such as in a picture parameter set)and a width equal to the width of the picture.

In some examples, a tile may be partitioned into multiple bricks, eachof which may include one or more CTU rows within the tile. A tile thatis not partitioned into multiple bricks may also be referred to as abrick. However, a brick that is a true subset of a tile may not bereferred to as a tile. The bricks in a picture may also be arranged in aslice. A slice may be an integer number of bricks of a picture that maybe exclusively contained in a single network abstraction layer (NAL)unit. In some examples, a slice includes either a number of completetiles or only a consecutive sequence of complete bricks of one tile.

This disclosure may use “N×N” and “N by N” interchangeably to refer tothe sample dimensions of a block (such as a CU or other video block) interms of vertical and horizontal dimensions, e.g., 16×16 samples or 16by 16 samples. In general, a 16×16 CU will have 16 samples in a verticaldirection (y=16) and 16 samples in a horizontal direction (x=16).Likewise, an N×N CU generally has N samples in a vertical direction andN samples in a horizontal direction, where N represents a nonnegativeinteger value. The samples in a CU may be arranged in rows and columns.Moreover, CUs need not necessarily have the same number of samples inthe horizontal direction as in the vertical direction. For example, CUsmay comprise N×M samples, where M is not necessarily equal to N.

Video encoder 200 encodes video data for CUs representing predictionand/or residual information, and other information. The predictioninformation indicates how the CU is to be predicted in order to form aprediction block for the CU. The residual information generallyrepresents sample-by-sample differences between samples of the CU priorto encoding and the prediction block.

To predict a CU, video encoder 200 may generally form a prediction blockfor the CU through inter-prediction or intra-prediction.Inter-prediction generally refers to predicting the CU from data of apreviously coded picture, whereas intra-prediction generally refers topredicting the CU from previously coded data of the same picture. Toperform inter-prediction, video encoder 200 may generate the predictionblock using one or more motion vectors. Video encoder 200 may generallyperform a motion search to identify a reference block that closelymatches the CU, e.g., in terms of differences between the CU and thereference block. Video encoder 200 may calculate a difference metricusing a sum of absolute difference (SAD), sum of squared differences(SSD), mean absolute difference (MAD), mean squared differences (MSD),or other such difference calculations to determine whether a referenceblock closely matches the current CU. In some examples, video encoder200 may predict the current CU using uni-directional prediction orbi-directional prediction.

Some examples of VVC also provide an affine motion compensation mode,which may be considered an inter-prediction mode. In affine motioncompensation mode, video encoder 200 may determine two or more motionvectors that represent non-translational motion, such as zoom in or out,rotation, perspective motion, or other irregular motion types.

To perform intra-prediction, video encoder 200 may select anintra-prediction mode to generate the prediction block. Some examples ofVVC provide sixty-seven intra-prediction modes, including variousdirectional modes, as well as planar mode and DC mode. In general, videoencoder 200 selects an intra-prediction mode that describes neighboringsamples to a current block (e.g., a block of a CU) from which to predictsamples of the current block. Such samples may generally be above, aboveand to the left, or to the left of the current block in the same pictureas the current block, assuming video encoder 200 codes CTUs and CUs inraster scan order (left to right, top to bottom).

Video encoder 200 encodes data representing the prediction mode for acurrent block. For example, for inter-prediction modes, video encoder200 may encode data representing which of the various availableinter-prediction modes is used, as well as motion information for thecorresponding mode. For uni-directional or bi-directionalinter-prediction, for example, video encoder 200 may encode motionvectors using advanced motion vector prediction (AMVP) or merge mode.Video encoder 200 may use similar modes to encode motion vectors foraffine motion compensation mode.

AV1 includes two general techniques for encoding and decoding a codingblock of video data. The two general techniques are intra prediction(e.g., intra frame prediction or spatial prediction) and interprediction (e.g., inter frame prediction or temporal prediction). In thecontext of AV1, when predicting blocks of a current frame of video datausing an intra prediction mode, video encoder 200 and video decoder 300do not use video data from other frames of video data. For most intraprediction modes, video encoder 200 encodes blocks of a current framebased on the difference between sample values in the current block andpredicted values generated from reference samples in the same frame.Video encoder 200 determines predicted values generated from thereference samples based on the intra prediction mode.

Following prediction, such as intra-prediction or inter-prediction of ablock, video encoder 200 may calculate residual data for the block. Theresidual data, such as a residual block, represents sample by sampledifferences between the block and a prediction block for the block,formed using the corresponding prediction mode. Video encoder 200 mayapply one or more transforms to the residual block, to producetransformed data in a transform domain instead of the sample domain. Forexample, video encoder 200 may apply a discrete cosine transform (DCT),an integer transform, a wavelet transform, or a conceptually similartransform to residual video data. Additionally, video encoder 200 mayapply a secondary transform following the first transform, such as amode-dependent non-separable secondary transform (MDNSST), a signaldependent transform, a Karhunen-Loeve transform (KLT), or the like.Video encoder 200 produces transform coefficients following applicationof the one or more transforms.

As noted above, following any transforms to produce transformcoefficients, video encoder 200 may perform quantization of thetransform coefficients. Quantization generally refers to a process inwhich transform coefficients are quantized to possibly reduce the amountof data used to represent the transform coefficients, providing furthercompression. By performing the quantization process, video encoder 200may reduce the bit depth associated with some or all of the transformcoefficients. For example, video encoder 200 may round an n-bit valuedown to an m-bit value during quantization, where n is greater than m.In some examples, to perform quantization, video encoder 200 may performa bitwise right-shift of the value to be quantized.

Following quantization, video encoder 200 may scan the transformcoefficients, producing a one-dimensional vector from thetwo-dimensional matrix including the quantized transform coefficients.The scan may be designed to place higher energy (and therefore lowerfrequency) transform coefficients at the front of the vector and toplace lower energy (and therefore higher frequency) transformcoefficients at the back of the vector. In some examples, video encoder200 may utilize a predefined scan order to scan the quantized transformcoefficients to produce a serialized vector, and then entropy encode thequantized transform coefficients of the vector. In other examples, videoencoder 200 may perform an adaptive scan. After scanning the quantizedtransform coefficients to form the one-dimensional vector, video encoder200 may entropy encode the one-dimensional vector, e.g., according tocontext-adaptive binary arithmetic coding (CABAC). Video encoder 200 mayalso entropy encode values for syntax elements describing metadataassociated with the encoded video data for use by video decoder 300 indecoding the video data.

To perform CABAC, video encoder 200 may assign a context within acontext model to a symbol to be transmitted. The context may relate to,for example, whether neighboring values of the symbol are zero-valued ornot. The probability determination may be based on a context assigned tothe symbol.

Video encoder 200 may further generate syntax data, such as block-basedsyntax data, picture-based syntax data, and sequence-based syntax data,to video decoder 300, e.g., in a picture header, a block header, a sliceheader, or other syntax data, such as a sequence parameter set (SPS),picture parameter set (PPS), or video parameter set (VPS). Video decoder300 may likewise decode such syntax data to determine how to decodecorresponding video data.

In this manner, video encoder 200 may generate a bitstream includingencoded video data, e.g., syntax elements describing partitioning of apicture into blocks (e.g., CUs) and prediction and/or residualinformation for the blocks. Ultimately, video decoder 300 may receivethe bitstream and decode the encoded video data.

In general, video decoder 300 performs a reciprocal process to thatperformed by video encoder 200 to decode the encoded video data of thebitstream. For example, video decoder 300 may decode values for syntaxelements of the bitstream using CABAC in a manner substantially similarto, albeit reciprocal to, the CABAC encoding process of video encoder200. The syntax elements may define partitioning information forpartitioning of a picture into CTUs, and partitioning of each CTUaccording to a corresponding partition structure, such as a QTBTstructure, to define CUs of the CTU. The syntax elements may furtherdefine prediction and residual information for blocks (e.g., CUs) ofvideo data.

The residual information may be represented by, for example, quantizedtransform coefficients. Video decoder 300 may inverse quantize andinverse transform the quantized transform coefficients of a block toreproduce a residual block for the block. Video decoder 300 uses asignaled prediction mode (intra- or inter-prediction) and relatedprediction information (e.g., motion information for inter-prediction)to form a prediction block for the block. Video decoder 300 may thencombine the prediction block and the residual block (on asample-by-sample basis) to reproduce the original block. Video decoder300 may perform additional processing, such as performing a deblockingprocess to reduce visual artifacts along boundaries of the block.

This disclosure may generally refer to “signaling” certain information,such as syntax elements. The term “signaling” may generally refer to thecommunication of values for syntax elements and/or other data used todecode encoded video data. That is, video encoder 200 may signal valuesfor syntax elements in the bitstream. In general, signaling refers togenerating a value in the bitstream. As noted above, source device 102may transport the bitstream to destination device 116 substantially inreal time, or not in real time, such as might occur when storing syntaxelements to storage device 112 for later retrieval by destination device116.

In accordance with the techniques of this disclosure, __.

FIG. 2 is a conceptual diagram illustrating a hybrid video codingframework. Video coding standards since H.261 have been based on theso-called hybrid video coding principle, which is illustrated in FIG. 2. The term hybrid refers to the combination of two means to reduceredundancy in the video signal, i.e., prediction and transform codingwith quantization of the prediction residual. Whereas prediction andtransforms reduce redundancy in the video signal by decorrelation,quantization decreases the data of the transform coefficientrepresentation by reducing their precision, ideally by removing onlyirrelevant details. This hybrid video coding design principle is alsoused in the two recent standards, ITU-T H.265/HEVC and ITU-T H.266/VVC.

As shown in FIG. 2 , a modern hybrid video coder 130 generally performsblock partitioning, motion-compensated or inter-picture prediction,intra-picture prediction, transformation, quantization, entropy coding,and post/in-loop filtering. In the example of FIG. 2 , video coder 130includes summation unit 134, transform unit 136, quantization unit 138,entropy coding unit 140, inverse quantization unit 142, inversetransform unit 144, summation unit 146, loop filter unit 148, decodedpicture buffer (DPB) 150, intra prediction unit 152, inter-predictionunit 154, and motion estimation unit 156.

In general, video coder 130 may, when encoding video data, receive inputvideo data 132. Block partitioning is used to divide a received picture(image) of the video data into smaller blocks for operation of theprediction and transform processes. Early video coding standards used afixed block size, typically 16×16 samples. Recent standards, such asHEVC and VVC, employ tree-based partitioning structures to provideflexible partitioning.

Motion estimation unit 156 and inter-prediction unit 154 may predictinput video data 132, e.g., from previously decoded data of DPB 150.Motion-compensated or inter-picture prediction takes advantage of theredundancy that exists between (hence “inter”) pictures of a videosequence. According to block-based motion compensation, which is used inall the modern video codecs, the prediction is obtained from one or morepreviously decoded pictures, i.e., the reference picture(s). Thecorresponding areas to generate the inter-prediction are indicated bymotion information, including motion vectors and reference pictureindices.

Summation unit 134 may calculate residual data as differences betweeninput video data 132 and predicted data from intra prediction unit 152or inter-prediction unit 154. Summation unit 134 provides residualblocks to transform unit 136, which applies one or more transforms tothe residual block to generate transform blocks. Quantization unit 138quantizes the transform blocks to form quantized transform coefficients.Entropy coding unit 140 entropy encodes the quantized transformcoefficients, as well as other syntax elements, such as motioninformation or intra-prediction information, to generate outputbitstream 158.

Meanwhile, inverse quantization unit 142 inverse quantizes the quantizedtransform coefficients, and inverse transform unit 144 inversetransforms the transform coefficients, to reproduce residual blocks.Summation unit 146 combines the residual blocks with prediction blocks(on a sample-by-sample basis) to produce decoded blocks of video data.Loop filter unit 148 applies one or more filters (e.g., at least one ofa neural network-based filter, a neural network-based loop filter, aneural network-based post loop filter, an adaptive in-loop filter, or apre-defined adaptive in-loop filter) to the decoded block to producefiltered decoded blocks.

In accordance with the techniques of this disclosure, a neural networkfiltering unit of loop filter unit 148 may receive data for a decodedpicture of video data from summation unit 146 and from one or more otherunits of hybrid video coder 130, e.g., transform unit 136, quantizationunit 138, intra prediction unit 152, inter-prediction unit 154, motionestimation unit 156, and/or one or more other filtering units withinloop filter unit 148. For example, the neural network filtering unit mayreceive data from a deblocking filtering unit (also referred to as a“deblocking unit) of loop filter unit 148. The neural network filteringunit may receive, for example, boundary strength values representingwhether a particular boundary is to be filtered for deblocking, and ifso, a degree to which the boundary will be filtered. For example, theboundary strength values may correspond to a number of samples on eitherside of the boundary to be modified and/or a degree to which the samplesare to be modified.

In other examples, in addition to or in the alternative to the boundarystrength values, the neural network filtering unit may receive any orall of coding unit (CU) partitioning data, prediction unit (PU)partitioning data, transform unit (TU) partitioning data, deblockingfiltering data, quantization parameter (QP) data, intra-prediction data,inter-prediction data, data representing distance between the decodedpicture and one or more reference pictures, or motion information forone or more decoded blocks of the decoded picture. The deblockingfiltering data may further include one or more of whether long or shortfilters were used for deblocking or whether strong or weak filters wereused for deblocking. The data representing the distance between thedecoded picture and the reference pictures may be represented as pictureorder count (POC) differences between POC values of the pictures.

The neural network filtering unit may determine one or more neuralnetwork models to be used to filter at least a portion of the decodedpicture. The neural network filtering unit may further filter the atleast portion of the decoded picture using the determined one or moreneural network models and the data from the other units, including theboundary strength data. For example, the neural network filtering unitmay provide the additional data as one or more additional input planesto a convolutional neural network (CNN).

A block of video data, such as a CTU or CU, may in fact include multiplecolor components, e.g., a luminance or “luma” component, a blue huechrominance or “chroma” component, and a red hue chrominance (chroma)component. The luma component may have a larger spatial resolution thanthe chroma components, and one of the chroma components may have alarger spatial resolution than the other chroma component.Alternatively, the luma component may have a larger spatial resolutionthan the chroma components, and the two chroma components may have equalspatial resolutions with each other. For example, in 4:2:2 format, theluma component may be twice as large as the chroma componentshorizontally and equal to the chroma components vertically. As anotherexample, in 4:2:0 format, the luma component may be twice as large asthe chroma components horizontally and vertically. The variousoperations discussed above may generally be applied to each of the lumaand chroma components individually (although certain coding information,such as motion information or intra-prediction direction, may bedetermined for the luma component and inherited by the correspondingchroma components).

FIG. 3 is a conceptual diagram illustrating a hierarchical predictionstructure 166 using a group of pictures (GOP) size of 16. In recentvideo codecs, hierarchical prediction structures inside a group ofpictures (GOP) is applied to improve coding efficiency.

Referring again to FIG. 2 , intra-picture prediction exploits spatialredundancy that exists within a picture (hence “intra”) by deriving theprediction for a block from already coded/decoded, spatially neighboring(reference) samples. The directional angular prediction, DC predictionand plane or planar prediction are used in the most recent video codec,including AVC, HEVC, and VVC.

Hybrid video coding standards apply a block transform to the predictionresidual (regardless of whether it comes from inter- or intra-pictureprediction). In early standards, including H.261, H.262, and H.263, adiscrete cosine transform (DCT) is employed. In HEVC and VVC, moretransform kernel besides DCT are applied, in order to account fordifferent statistics in the specific video signal.

Quantization aims to reduce the precision of an input value or a set ofinput values in order to decrease the amount of data needed to representthe values. In hybrid video coding, quantization is typically applied toindividual transformed residual samples, i.e., to transformcoefficients, resulting in integer coefficient levels. In recent videocoding standards, the step size is derived from a so-called quantizationparameter (QP) that controls the fidelity and bit rate. A larger stepsize lowers the bit rate but also deteriorates the quality, which e.g.,results in video pictures exhibiting blocking artifacts and blurreddetails.

Context-adaptive binary arithmetic coding (CABAC) is a form of entropycoding used in recent video codecs, e.g., AVC, HEVC, and VVC, due to itshigh efficiency.

Post/in-loop filtering is a filtering process (or combination of suchprocesses) that is applied to the reconstructed picture to reduce thecoding artifacts. The input of the filtering process is generally thereconstructed picture, which is the combination of the reconstructedresidual signal (which includes quantization error) and the prediction.As shown in FIG. 2 , the reconstructed pictures after in-loop filteringare stored and used as a reference for inter-picture prediction ofsubsequent pictures. The coding artifacts are mostly determined by theQP, therefore QP information is generally used in design of thefiltering process. In HEVC, the in-loop filters include deblockingfiltering and sample adaptive offset (SAO) filtering. In the VVCstandard, an adaptive loop filter (ALF) was introduced as a thirdfilter. The filtering process of ALF is as shown below:

R′(i,j)=R(i,j)+((Σ_(k≠0)Σ_(l≠0)f(k,l)×K(R(i+k,j+l)−R(i,j),c(k,l))+64)>>7)

((Σ_(k≠0)Σ_(l≠0) f(k,l)×K(R(i+k,j+l)−R(i,j),c(k,l))+64)>>7)  (1)

where R(i,j) is the set of samples before the filtering process, R′(i,j)is a sample value after the filtering process. f(k,l) denotes filtercoefficients, K(x,y) is a clipping function and c(k,l) denotes theclipping parameters. The variables k and 1 vary between −L/2 and L/2where L denotes the filter length. The clipping function K(x,y)=min(y,max(−y,x)), which corresponds to the function Clip3 (−y,y,x). Theclipping operation introduces non-linearity to make ALF more efficientby reducing the impact of neighbor sample values that are too differentwith the current sample value. In VVC, the filtering parameters can besignalled in the bit stream, it can be selected from the pre-definedfilter sets. The ALF filtering process can also be summarized using thefollowing equation:

R′(i,j)=R(i,j)+ALF_residual_ouput(R)  (2)

FIG. 4 is a conceptual diagram illustrating a neural network basedfilter 170 with four layers. Various studies have shown that embeddingneural networks (NN) into, e.g., the hybrid video coding framework ofFIG. 2 , can improve compression efficiency. Neural networks have beenused in the module of intra prediction and inter-prediction to improveprediction efficiency. NN-based in loop filtering is also a hot researchtopic in recent years. Sometime the filtering process is applied aspost-loop filtering. in this case, the filtering process is only appliedto the output picture and the un-filtered picture is used as referencepicture.

NN-based filter 170 can be applied in addition to the existing filters,such as deblocking filters, sample adaptive offset (SAO), and/oradaptive loop filtering (ALF). NN-based filters can also be appliedexclusively, where NN-based filters are designed to replace all of theexisting filters. Additionally or alternatively, NN-based filters, suchas NN-based filter 170, may be designed to supplement, enhance, orreplace any or all of the other filters.

As shown in FIG. 4 , the NN-based filtering process may take thereconstructed samples as inputs, and the intermediate outputs areresidual samples, which are added back to the input to refine the inputsamples. The NN filter may use all color components (e.g., Y, U, and V,or Y, Cb, and Cr, i.e., luminance, blue-hue chrominance, and red-huechrominance) as input to exploit cross-component correlations. Differentcolor components may share the same filters (including network structureand model parameters) or each component may have its own specificfilters.

The filtering process can also be generalized as follows:R′(i,j)=R(i,j)+NN_filter_residual_ouput(R). The model structure andmodel parameters of NN-based filter(s) can pre-defined and be stored atencoder and decoder. The filters can also be signalled in the bitstream.

FIG. 5 is a conceptual diagram illustrating an example neural network(NN)-based filtering solution with multiple modes. That is, FIG. 5depicts an example NN-based filter with multiple modes. The NN-basedfilter includes a first portion including input 3×3 convolution filters510A-510E, respective parametric rectified linear unit (PReLU) filters512A-512E, concatenation unit 514, fuse filter 516 and transition filter522; a set 528 of attention residual (AttRes) blocks 530A-530N; and alast portion including 3×3 convolution filter 550, PReLU filter 552, 3×3convolution filter 554, and pixel shuffle unit 556.

In the first portion, different inputs, including quantization parameter(QP) 500, partition information (part) 502, boundary strength (BS) 504,prediction information (pred) 506, and reconstructed block (rec) 508 arereceived. Respective 3×3 convolution filters 510A-510E and PReLU filters512A-512E convolve the respective inputs. Concatenation unit 514 thenconcatenates the convolved inputs. Fuse filter 516, including 1×1convolution filter 518 and PReLU filter 520, fuses the concatenatedinputs. Transition filter, including 3×3 convolutional filter 524 andPReLU filter 526, subsamples the fused inputs to create output 188.Output 188 is then fed through set 528 of attention residual blocks530A-530N, which may include a various number of attention residualblocks, e.g., 8. The attention block is explained further with respectto FIG. 6 . Output 189 from the last of the set of attention residualblocks 184 is fed to the last portion. In the last portion, 3×3convolution filter 550, PReLU filter 552, 3×3 convolution filter 554,and pixel shuffle unit 556 processes output 189, and addition unit 558combines this result with the original reconstructed block input 508.This ultimately forms output for presentation and storage as referencefor subsequent inter-prediction, e.g., in a decoded picture buffer(DPB).

In some examples, this NN-based filter uses 96 feature maps. The examplemodel of FIG. 5 has a relatively high complexity in terms of KMAC/pixel.

FIG. 6 is a conceptual diagram illustrating an attention residual blockof FIG. 5 . That is, FIG. 6 depicts attention residual block 530, whichmay include components similar to those of attention residual blocks530A-530N of FIG. 5 . In this example, attention residual block 530includes first 3×3 convolutional filter 532, parametric rectified linearunit (PReLU) filter 534, second 3×3 convolutional filter 536, anattention block 538, and addition unit 540. Addition unit 540 combinesthe output of attention block 538 and output 188, initially received byconvolution filter 532, to generate output 189.

FIG. 7 is a conceptual diagram illustrating an example spatial attentionlayer of FIG. 7 . As shown in FIG. 7 , a spatial attention layer ofattention residual block 530 includes 3×3 convolutional filter 706,PReLU filter 708, 3×3 convolution filter 710, size expansion unit 712,3×3 convolution filter 720, PReLU filter 722, and 3×3 convolution filter724. 3×3 convolution filter 706 receives inputs 702, corresponding toquantization parameter (QP) 500, partition information (part) 502,boundary strength (BS) 504, prediction information (pred) 506, andreconstructed block (rec) 508 of FIG. 5 . 3×3 convolution filter 720receives Z_(K) 704. The outputs of size expansion unit 712 and 3×3convolution filter 724 are combined, and then combined with R value 730to generate S value 732. S value 732 is then combined with Z_(K) value704 to generate output Z_(K+1) value 734.

FIG. 8 is a conceptual diagram illustrating an example residual blockstructure that may be substituted for the attention residual blocks ofFIG. 5 according to the techniques of this disclosure. The NN basedfilter of FIG. 8 includes 3×3 convolution filters 810A-810E and PReLUfilters 812A-812E, which convolve respective inputs, i.e., QP 800, Part802, BS 804, Pred 806, and Rec 808. Concatenation unit 814 concatenatesthe convolved inputs. Fuse filter 816 then fuses the concatenated inputsusing 1×1 convolution filter 818 and PReLU filter 820. Transition unit822 then processes the fused data using 3×3 convolution filter 824 andPReLU filter 826.

In this example, the NN based filter includes a set 828 of residualblocks 830A-830N, each of which may be structured according to residualblock structure 830 of FIG. 9 , as discussed below. Residual blocks830A-830N may replace AttRes blocks 530A-530N of FIG. 5 . The example ofFIG. 8 may be used for luminance (luma) filtering, although as discussedbelow, similar modifications may be made for chrominance (chroma)filtering.

The number of residual blocks and channels included in set 828 of FIG. 8can be configured differently. That is, N may be set to a differentvalue, and the number of channels in residual block structure 830 may beset to a number different than 160, to achieve differentperformance-complexity tradeoffs. Chroma filtering may be performed withthese modifications for processing of chroma channels.

Set 828 of residual blocks 830A-830N has N instances of residual blockstructure 830. In one example, N may be equal to 32, such that there are32 residual block structures. Residual blocks 830A-830N may use 64feature maps, which is reduced relative to the 96 feature maps used inthe example of FIG. 5 .

FIG. 9 is a conceptual diagram illustrating an example residual blockstructure 830 of FIG. 8 . In this example, residual block structure 830includes first 1×1 convolutional filter 832, which may increase a numberof input channels to 160, before an activation layer (PReLU filter 834)processes the input channels. PReLU filter 834 may thereby reduce thenumber of channels to 64 through this processing. Second 1×1 convolutionfilter 836 then processes the reduced channels, followed by 3×3convolution filter 838. Finally, combination unit 840 may combine theoutput of 3×3 convolution filter 838 with the original input received byresidual block structure 830.

FIG. 10 is a conceptual diagram illustrating another example filteringblock structure that may be substituted for the set of attentionresidual blocks of FIG. 5 according to the techniques of thisdisclosure. The NN based filter of FIG. 10 includes 3×3 convolutionfilters 1010A-1010E and PReLU filters 1012A-1012E, which convolverespective inputs, i.e., QP 1000, Part 1002, BS 1004, Pred 1006, and Rec1008. Concatenation unit 1014 concatenates the convolved inputs. Fusefilter 1016 then fuses the concatenated inputs using 1×1 convolutionfilter 1018 and PReLU filter 1020. Transition unit 1022 then processesthe fused data using 3×3 convolution filter 1024 and PReLU filter 1026.

In this example, the NN-based filtering unit includes N filter blocks1030A-1030N, each of which may have the structure of filter block 1030of FIG. 11 as discussed below. Filter block structure 1030 may besubstantially similar to residual block structure 830, except thatcombination unit 840 is omitted from filter block structure 1030, suchthat input is not combined with output. Instead, output of each residualblock structure may be fed directly to the subsequent block.

The number of channels and number of filter blocks may be configurable.In one example, it could be set to 64 channels and 32 filter blocks. Thenumber of increased channels in each filter block 198 may be 160 asdiscussed above.

FIG. 11 is a conceptual diagram illustrating an example filter blockstructure 1030 of FIG. 10 . In this example, residual block structure1030 includes first 1×1 convolutional filter 1032, which may increase anumber of input channels to 160, before an activation layer (PReLUfilter 1034) processes the input channels. PReLU filter 1034 may therebyreduce the number of channels to 64 through this processing. Second 1×1convolution filter 1036 then processes the reduced channels, followed by3×3 convolution filter 1038. As discussed above, filter block structure1030 does not include a combination unit, in contrast with the residualblock structure 830 of FIG. 9 .

FIG. 12 is a block diagram illustrating an example video encoder 200that may perform the techniques of this disclosure. FIG. 12 is providedfor purposes of explanation and should not be considered limiting of thetechniques as broadly exemplified and described in this disclosure. Forpurposes of explanation, this disclosure describes video encoder 200according to the techniques of VVC (ITU-T H.266, under development) andHEVC (ITU-T H.265). However, the techniques of this disclosure may beperformed by video encoding devices that are configured to other videocoding standards and video coding formats, such as AV1 and successors tothe AV1 video coding format.

In the example of FIG. 12 , video encoder 200 includes video data memory230, mode selection unit 202, residual generation unit 204, transformprocessing unit 206, quantization unit 208, inverse quantization unit210, inverse transform processing unit 212, reconstruction unit 214,filter unit 216, decoded picture buffer (DPB) 218, and entropy encodingunit 220. Any or all of video data memory 230, mode selection unit 202,residual generation unit 204, transform processing unit 206,quantization unit 208, inverse quantization unit 210, inverse transformprocessing unit 212, reconstruction unit 214, filter unit 216, DPB 218,and entropy encoding unit 220 may be implemented in one or moreprocessors or in processing circuitry. For instance, the units of videoencoder 200 may be implemented as one or more circuits or logic elementsas part of hardware circuitry, or as part of a processor, ASIC, or FPGA.Moreover, video encoder 200 may include additional or alternativeprocessors or processing circuitry to perform these and other functions.

Video data memory 230 may store video data to be encoded by thecomponents of video encoder 200. Video encoder 200 may receive the videodata stored in video data memory 230 from, for example, video source 104(FIG. 1 ). DPB 218 may act as a reference picture memory that storesreference video data for use in prediction of subsequent video data byvideo encoder 200. Video data memory 230 and DPB 218 may be formed byany of a variety of memory devices, such as dynamic random access memory(DRAM), including synchronous DRAM (SDRAM), magnetoresistive RAM (MRAM),resistive RAM (RRAM), or other types of memory devices. Video datamemory 230 and DPB 218 may be provided by the same memory device orseparate memory devices. In various examples, video data memory 230 maybe on-chip with other components of video encoder 200, as illustrated,or off-chip relative to those components.

In this disclosure, reference to video data memory 230 should not beinterpreted as being limited to memory internal to video encoder 200,unless specifically described as such, or memory external to videoencoder 200, unless specifically described as such. Rather, reference tovideo data memory 230 should be understood as reference memory thatstores video data that video encoder 200 receives for encoding (e.g.,video data for a current block that is to be encoded). Memory 106 ofFIG. 1 may also provide temporary storage of outputs from the variousunits of video encoder 200.

The various units of FIG. 12 are illustrated to assist withunderstanding the operations performed by video encoder 200. The unitsmay be implemented as fixed-function circuits, programmable circuits, ora combination thereof. Fixed-function circuits refer to circuits thatprovide particular functionality, and are preset on the operations thatcan be performed. Programmable circuits refer to circuits that can beprogrammed to perform various tasks, and provide flexible functionalityin the operations that can be performed. For instance, programmablecircuits may execute software or firmware that cause the programmablecircuits to operate in the manner defined by instructions of thesoftware or firmware. Fixed-function circuits may execute softwareinstructions (e.g., to receive parameters or output parameters), but thetypes of operations that the fixed-function circuits perform aregenerally immutable. In some examples, one or more of the units may bedistinct circuit blocks (fixed-function or programmable), and in someexamples, one or more of the units may be integrated circuits.

Video encoder 200 may include arithmetic logic units (ALUs), elementaryfunction units (EFUs), digital circuits, analog circuits, and/orprogrammable cores, formed from programmable circuits. In examples wherethe operations of video encoder 200 are performed using softwareexecuted by the programmable circuits, memory 106 (FIG. 1 ) may storethe instructions (e.g., object code) of the software that video encoder200 receives and executes, or another memory within video encoder 200(not shown) may store such instructions.

Video data memory 230 is configured to store received video data. Videoencoder 200 may retrieve a picture of the video data from video datamemory 230 and provide the video data to residual generation unit 204and mode selection unit 202. Video data in video data memory 230 may beraw video data that is to be encoded.

Mode selection unit 202 includes a motion estimation unit 222, a motioncompensation unit 224, and an intra-prediction unit 226. Mode selectionunit 202 may include additional functional units to perform videoprediction in accordance with other prediction modes. As examples, modeselection unit 202 may include a palette unit, an intra-block copy unit(which may be part of motion estimation unit 222 and/or motioncompensation unit 224), an affine unit, a linear model (LM) unit, or thelike.

Mode selection unit 202 generally coordinates multiple encoding passesto test combinations of encoding parameters and resultingrate-distortion values for such combinations. The encoding parametersmay include partitioning of CTUs into CUs, prediction modes for the CUs,transform types for residual data of the CUs, quantization parametersfor residual data of the CUs, and so on. Mode selection unit 202 mayultimately select the combination of encoding parameters havingrate-distortion values that are better than the other testedcombinations.

Video encoder 200 may partition a picture retrieved from video datamemory 230 into a series of CTUs, and encapsulate one or more CTUswithin a slice. Mode selection unit 202 may partition a CTU of thepicture in accordance with a tree structure, such as the MTT structure,QTBT structure. superblock structure, or the quad-tree structuredescribed above. As described above, video encoder 200 may form one ormore CUs from partitioning a CTU according to the tree structure. Such aCU may also be referred to generally as a “video block” or “block.”

In general, mode selection unit 202 also controls the components thereof(e.g., motion estimation unit 222, motion compensation unit 224, andintra-prediction unit 226) to generate a prediction block for a currentblock (e.g., a current CU, or in HEVC, the overlapping portion of a PUand a TU). For inter-prediction of a current block, motion estimationunit 222 may perform a motion search to identify one or more closelymatching reference blocks in one or more reference pictures (e.g., oneor more previously coded pictures stored in DPB 218). In particular,motion estimation unit 222 may calculate a value representative of howsimilar a potential reference block is to the current block, e.g.,according to sum of absolute difference (SAD), sum of squareddifferences (SSD), mean absolute difference (MAD), mean squareddifferences (MSD), or the like. Motion estimation unit 222 may generallyperform these calculations using sample-by-sample differences betweenthe current block and the reference block being considered. Motionestimation unit 222 may identify a reference block having a lowest valueresulting from these calculations, indicating a reference block thatmost closely matches the current block.

Motion estimation unit 222 may form one or more motion vectors (MVs)that defines the positions of the reference blocks in the referencepictures relative to the position of the current block in a currentpicture. Motion estimation unit 222 may then provide the motion vectorsto motion compensation unit 224. For example, for uni-directionalinter-prediction, motion estimation unit 222 may provide a single motionvector, whereas for bi-directional inter-prediction, motion estimationunit 222 may provide two motion vectors. Motion compensation unit 224may then generate a prediction block using the motion vectors. Forexample, motion compensation unit 224 may retrieve data of the referenceblock using the motion vector. As another example, if the motion vectorhas fractional sample precision, motion compensation unit 224 mayinterpolate values for the prediction block according to one or moreinterpolation filters. Moreover, for bi-directional inter-prediction,motion compensation unit 224 may retrieve data for two reference blocksidentified by respective motion vectors and combine the retrieved data,e.g., through sample-by-sample averaging or weighted averaging.

When operating according to the AV1 video coding format, motionestimation unit 222 and motion compensation unit 224 may be configuredto encode coding blocks of video data (e.g., both luma and chroma codingblocks) using translational motion compensation, affine motioncompensation, overlapped block motion compensation (OBMC), and/orcompound inter-intra prediction.

As another example, for intra-prediction, or intra-prediction coding,intra-prediction unit 226 may generate the prediction block from samplesneighboring the current block. For example, for directional modes,intra-prediction unit 226 may generally mathematically combine values ofneighboring samples and populate these calculated values in the defineddirection across the current block to produce the prediction block. Asanother example, for DC mode, intra-prediction unit 226 may calculate anaverage of the neighboring samples to the current block and generate theprediction block to include this resulting average for each sample ofthe prediction block.

When operating according to the AV1 video coding format, intraprediction unit 226 may be configured to encode coding blocks of videodata (e.g., both luma and chroma coding blocks) using directional intraprediction, non-directional intra prediction, recursive filter intraprediction, chroma-from-luma (CFL) prediction, intra block copy (IBC),and/or color palette mode. Mode selection unit 202 may includeadditional functional units to perform video prediction in accordancewith other prediction modes.

Mode selection unit 202 provides the prediction block to residualgeneration unit 204. Residual generation unit 204 receives a raw,uncoded version of the current block from video data memory 230 and theprediction block from mode selection unit 202. Residual generation unit204 calculates sample-by-sample differences between the current blockand the prediction block. The resulting sample-by-sample differencesdefine a residual block for the current block. In some examples,residual generation unit 204 may also determine differences betweensample values in the residual block to generate a residual block usingresidual differential pulse code modulation (RDPCM). In some examples,residual generation unit 204 may be formed using one or more subtractorcircuits that perform binary subtraction.

In examples where mode selection unit 202 partitions CUs into PUs, eachPU may be associated with a luma prediction unit and correspondingchroma prediction units. Video encoder 200 and video decoder 300 maysupport PUs having various sizes. As indicated above, the size of a CUmay refer to the size of the luma coding block of the CU and the size ofa PU may refer to the size of a luma prediction unit of the PU. Assumingthat the size of a particular CU is 2N×2N, video encoder 200 may supportPU sizes of 2N×2N or N×N for intra prediction, and symmetric PU sizes of2N×2N, 2N×N, N×2N, N×N, or similar for inter prediction. Video encoder200 and video decoder 300 may also support asymmetric partitioning forPU sizes of 2N×nU, 2N×nD, nL×2N, and nR×2N for inter prediction.

In examples where mode selection unit 202 does not further partition aCU into PUs, each CU may be associated with a luma coding block andcorresponding chroma coding blocks. As above, the size of a CU may referto the size of the luma coding block of the CU. The video encoder 200and video decoder 300 may support CU sizes of 2N×2N, 2N×N, or N×2N.

For other video coding techniques such as an intra-block copy modecoding, an affine-mode coding, and linear model (LM) mode coding, assome examples, mode selection unit 202, via respective units associatedwith the coding techniques, generates a prediction block for the currentblock being encoded. In some examples, such as palette mode coding, modeselection unit 202 may not generate a prediction block, and insteadgenerate syntax elements that indicate the manner in which toreconstruct the block based on a selected palette. In such modes, modeselection unit 202 may provide these syntax elements to entropy encodingunit 220 to be encoded.

As described above, residual generation unit 204 receives the video datafor the current block and the corresponding prediction block. Residualgeneration unit 204 then generates a residual block for the currentblock. To generate the residual block, residual generation unit 204calculates sample-by-sample differences between the prediction block andthe current block.

Transform processing unit 206 applies one or more transforms to theresidual block to generate a block of transform coefficients (referredto herein as a “transform coefficient block”). Transform processing unit206 may apply various transforms to a residual block to form thetransform coefficient block. For example, transform processing unit 206may apply a discrete cosine transform (DCT), a directional transform, aKarhunen-Loeve transform (KLT), or a conceptually similar transform to aresidual block. In some examples, transform processing unit 206 mayperform multiple transforms to a residual block, e.g., a primarytransform and a secondary transform, such as a rotational transform. Insome examples, transform processing unit 206 does not apply transformsto a residual block.

When operating according to AV1, transform processing unit 206 may applyone or more transforms to the residual block to generate a block oftransform coefficients (referred to herein as a “transform coefficientblock”). Transform processing unit 206 may apply various transforms to aresidual block to form the transform coefficient block. For example,transform processing unit 206 may apply a horizontal/vertical transformcombination that may include a discrete cosine transform (DCT), anasymmetric discrete sine transform (ADST), a flipped ADST (e.g., an ADSTin reverse order), and an identity transform (IDTX). When using anidentity transform, the transform is skipped in one of the vertical orhorizontal directions. In some examples, transform processing may beskipped.

Quantization unit 208 may quantize the transform coefficients in atransform coefficient block, to produce a quantized transformcoefficient block. Quantization unit 208 may quantize transformcoefficients of a transform coefficient block according to aquantization parameter (QP) value associated with the current block.Video encoder 200 (e.g., via mode selection unit 202) may adjust thedegree of quantization applied to the transform coefficient blocksassociated with the current block by adjusting the QP value associatedwith the CU. Quantization may introduce loss of information, and thus,quantized transform coefficients may have lower precision than theoriginal transform coefficients produced by transform processing unit206.

Inverse quantization unit 210 and inverse transform processing unit 212may apply inverse quantization and inverse transforms to a quantizedtransform coefficient block, respectively, to reconstruct a residualblock from the transform coefficient block. Reconstruction unit 214 mayproduce a reconstructed block corresponding to the current block (albeitpotentially with some degree of distortion) based on the reconstructedresidual block and a prediction block generated by mode selection unit202. For example, reconstruction unit 214 may add samples of thereconstructed residual block to corresponding samples from theprediction block generated by mode selection unit 202 to produce thereconstructed block.

Filter unit 216 may perform one or more filter operations onreconstructed blocks. For example, filter unit 216 may performdeblocking operations to reduce blockiness artifacts along edges of CUs.Operations of filter unit 216 may be skipped, in some examples. Filterunit 216 may include components similar to those of FIGS. 8-11 invarious examples.

When operating according to AV1, filter unit 216 may perform one or morefilter operations on reconstructed blocks. For example, filter unit 216may perform deblocking operations to reduce blockiness artifacts alongedges of CUs. In other examples, filter unit 216 may apply a constraineddirectional enhancement filter (CDEF), which may be applied afterdeblocking, and may include the application of non-separable,non-linear, low-pass directional filters based on estimated edgedirections. Filter unit 216 may also include a loop restoration filter,which is applied after CDEF, and may include a separable symmetricnormalized Wiener filter or a dual self-guided filter.

Video encoder 200 stores reconstructed blocks in DPB 218. For instance,in examples where operations of filter unit 216 are not performed,reconstruction unit 214 may store reconstructed blocks to DPB 218. Inexamples where operations of filter unit 216 are performed, filter unit216 may store the filtered reconstructed blocks to DPB 218. Motionestimation unit 222 and motion compensation unit 224 may retrieve areference picture from DPB 218, formed from the reconstructed (andpotentially filtered) blocks, to inter-predict blocks of subsequentlyencoded pictures. In addition, intra-prediction unit 226 may usereconstructed blocks in DPB 218 of a current picture to intra-predictother blocks in the current picture.

In general, entropy encoding unit 220 may entropy encode syntax elementsreceived from other functional components of video encoder 200. Forexample, entropy encoding unit 220 may entropy encode quantizedtransform coefficient blocks from quantization unit 208. As anotherexample, entropy encoding unit 220 may entropy encode prediction syntaxelements (e.g., motion information for inter-prediction or intra-modeinformation for intra-prediction) from mode selection unit 202. Entropyencoding unit 220 may perform one or more entropy encoding operations onthe syntax elements, which are another example of video data, togenerate entropy-encoded data. For example, entropy encoding unit 220may perform a context-adaptive variable length coding (CAVLC) operation,a CABAC operation, a variable-to-variable (V2V) length coding operation,a syntax-based context-adaptive binary arithmetic coding (SBAC)operation, a Probability Interval Partitioning Entropy (PIPE) codingoperation, an Exponential-Golomb encoding operation, or another type ofentropy encoding operation on the data. In some examples, entropyencoding unit 220 may operate in bypass mode where syntax elements arenot entropy encoded.

Video encoder 200 may output a bitstream that includes the entropyencoded syntax elements needed to reconstruct blocks of a slice orpicture. In particular, entropy encoding unit 220 may output thebitstream.

In accordance with AV1, entropy encoding unit 220 may be configured as asymbol-to-symbol adaptive multi-symbol arithmetic coder. A syntaxelement in AV1 includes an alphabet of N elements, and a context (e.g.,probability model) includes a set of N probabilities. Entropy encodingunit 220 may store the probabilities as n-bit (e.g., 15-bit) cumulativedistribution functions (CDFs). Entropy encoding unit 22 may performrecursive scaling, with an update factor based on the alphabet size, toupdate the contexts.

The operations described above are described with respect to a block.Such description should be understood as being operations for a lumacoding block and/or chroma coding blocks. As described above, in someexamples, the luma coding block and chroma coding blocks are luma andchroma components of a CU. In some examples, the luma coding block andthe chroma coding blocks are luma and chroma components of a PU.

In some examples, operations performed with respect to a luma codingblock need not be repeated for the chroma coding blocks. As one example,operations to identify a motion vector (MV) and reference picture for aluma coding block need not be repeated for identifying a MV andreference picture for the chroma blocks. Rather, the MV for the lumacoding block may be scaled to determine the MV for the chroma blocks,and the reference picture may be the same. As another example, theintra-prediction process may be the same for the luma coding block andthe chroma coding blocks.

FIG. 13 is a block diagram illustrating an example video decoder 300that may perform the techniques of this disclosure. FIG. 13 is providedfor purposes of explanation and is not limiting on the techniques asbroadly exemplified and described in this disclosure. For purposes ofexplanation, this disclosure describes video decoder 300 according tothe techniques of VVC (ITU-T H.266, under development) and HEVC (ITU-TH.265). However, the techniques of this disclosure may be performed byvideo coding devices that are configured to other video codingstandards.

In the example of FIG. 13 , video decoder 300 includes coded picturebuffer (CPB) memory 320, entropy decoding unit 302, predictionprocessing unit 304, inverse quantization unit 306, inverse transformprocessing unit 308, reconstruction unit 310, filter unit 312, anddecoded picture buffer (DPB) 314. Any or all of CPB memory 320, entropydecoding unit 302, prediction processing unit 304, inverse quantizationunit 306, inverse transform processing unit 308, reconstruction unit310, filter unit 312, and DPB 314 may be implemented in one or moreprocessors or in processing circuitry. For instance, the units of videodecoder 300 may be implemented as one or more circuits or logic elementsas part of hardware circuitry, or as part of a processor, ASIC, or FPGA.Moreover, video decoder 300 may include additional or alternativeprocessors or processing circuitry to perform these and other functions.

Prediction processing unit 304 includes motion compensation unit 316 andintra-prediction unit 318. Prediction processing unit 304 may includeadditional units to perform prediction in accordance with otherprediction modes. As examples, prediction processing unit 304 mayinclude a palette unit, an intra-block copy unit (which may form part ofmotion compensation unit 316), an affine unit, a linear model (LM) unit,or the like. In other examples, video decoder 300 may include more,fewer, or different functional components.

When operating according to AV1, motion compensation unit 316 may beconfigured to decode coding blocks of video data (e.g., both luma andchroma coding blocks) using translational motion compensation, affinemotion compensation, OBMC, and/or compound inter-intra prediction, asdescribed above. Intra prediction unit 318 may be configured to decodecoding blocks of video data (e.g., both luma and chroma coding blocks)using directional intra prediction, non-directional intra prediction,recursive filter intra prediction, CFL, intra block copy (IBC), and/orcolor palette mode, as described above.

CPB memory 320 may store video data, such as an encoded video bitstream,to be decoded by the components of video decoder 300. The video datastored in CPB memory 320 may be obtained, for example, fromcomputer-readable medium 110 (FIG. 1 ). CPB memory 320 may include a CPBthat stores encoded video data (e.g., syntax elements) from an encodedvideo bitstream. Also, CPB memory 320 may store video data other thansyntax elements of a coded picture, such as temporary data representingoutputs from the various units of video decoder 300. DPB 314 generallystores decoded pictures, which video decoder 300 may output and/or useas reference video data when decoding subsequent data or pictures of theencoded video bitstream. CPB memory 320 and DPB 314 may be formed by anyof a variety of memory devices, such as dynamic random access memory(DRAM), including synchronous DRAM (SDRAM), magnetoresistive RAM (MRAM),resistive RAM (RRAM), or other types of memory devices. CPB memory 320and DPB 314 may be provided by the same memory device or separate memorydevices. In various examples, CPB memory 320 may be on-chip with othercomponents of video decoder 300, or off-chip relative to thosecomponents.

Additionally or alternatively, in some examples, video decoder 300 mayretrieve coded video data from memory 120 (FIG. 1 ). That is, memory 120may store data as discussed above with CPB memory 320. Likewise, memory120 may store instructions to be executed by video decoder 300, whensome or all of the functionality of video decoder 300 is implemented insoftware to be executed by processing circuitry of video decoder 300.

The various units shown in FIG. 13 are illustrated to assist withunderstanding the operations performed by video decoder 300. The unitsmay be implemented as fixed-function circuits, programmable circuits, ora combination thereof. Similar to FIG. 12 , fixed-function circuitsrefer to circuits that provide particular functionality, and are preseton the operations that can be performed. Programmable circuits refer tocircuits that can be programmed to perform various tasks, and provideflexible functionality in the operations that can be performed. Forinstance, programmable circuits may execute software or firmware thatcause the programmable circuits to operate in the manner defined byinstructions of the software or firmware. Fixed-function circuits mayexecute software instructions (e.g., to receive parameters or outputparameters), but the types of operations that the fixed-functioncircuits perform are generally immutable. In some examples, one or moreof the units may be distinct circuit blocks (fixed-function orprogrammable), and in some examples, one or more of the units may beintegrated circuits.

Video decoder 300 may include ALUs, EFUs, digital circuits, analogcircuits, and/or programmable cores formed from programmable circuits.In examples where the operations of video decoder 300 are performed bysoftware executing on the programmable circuits, on-chip or off-chipmemory may store instructions (e.g., object code) of the software thatvideo decoder 300 receives and executes.

Entropy decoding unit 302 may receive encoded video data from the CPBand entropy decode the video data to reproduce syntax elements.Prediction processing unit 304, inverse quantization unit 306, inversetransform processing unit 308, reconstruction unit 310, and filter unit312 may generate decoded video data based on the syntax elementsextracted from the bitstream.

In general, video decoder 300 reconstructs a picture on a block-by-blockbasis. Video decoder 300 may perform a reconstruction operation on eachblock individually (where the block currently being reconstructed, i.e.,decoded, may be referred to as a “current block”).

Entropy decoding unit 302 may entropy decode syntax elements definingquantized transform coefficients of a quantized transform coefficientblock, as well as transform information, such as a quantizationparameter (QP) and/or transform mode indication(s). Inverse quantizationunit 306 may use the QP associated with the quantized transformcoefficient block to determine a degree of quantization and, likewise, adegree of inverse quantization for inverse quantization unit 306 toapply. Inverse quantization unit 306 may, for example, perform a bitwiseleft-shift operation to inverse quantize the quantized transformcoefficients. Inverse quantization unit 306 may thereby form a transformcoefficient block including transform coefficients.

After inverse quantization unit 306 forms the transform coefficientblock, inverse transform processing unit 308 may apply one or moreinverse transforms to the transform coefficient block to generate aresidual block associated with the current block. For example, inversetransform processing unit 308 may apply an inverse DCT, an inverseinteger transform, an inverse Karhunen-Loeve transform (KLT), an inverserotational transform, an inverse directional transform, or anotherinverse transform to the transform coefficient block.

Furthermore, prediction processing unit 304 generates a prediction blockaccording to prediction information syntax elements that were entropydecoded by entropy decoding unit 302. For example, if the predictioninformation syntax elements indicate that the current block isinter-predicted, motion compensation unit 316 may generate theprediction block. In this case, the prediction information syntaxelements may indicate a reference picture in DPB 314 from which toretrieve a reference block, as well as a motion vector identifying alocation of the reference block in the reference picture relative to thelocation of the current block in the current picture. Motioncompensation unit 316 may generally perform the inter-prediction processin a manner that is substantially similar to that described with respectto motion compensation unit 224 (FIG. 12 ).

As another example, if the prediction information syntax elementsindicate that the current block is intra-predicted, intra-predictionunit 318 may generate the prediction block according to anintra-prediction mode indicated by the prediction information syntaxelements. Again, intra-prediction unit 318 may generally perform theintra-prediction process in a manner that is substantially similar tothat described with respect to intra-prediction unit 226 (FIG. 12 ).Intra-prediction unit 318 may retrieve data of neighboring samples tothe current block from DPB 314.

Reconstruction unit 310 may reconstruct the current block using theprediction block and the residual block. For example, reconstructionunit 310 may add samples of the residual block to corresponding samplesof the prediction block to reconstruct the current block.

Filter unit 312 may perform one or more filter operations onreconstructed blocks. For example, filter unit 312 may performdeblocking operations to reduce blockiness artifacts along edges of thereconstructed blocks. Operations of filter unit 312 are not necessarilyperformed in all examples. Filter unit 312 may include componentssimilar to those of FIGS. 8-11 in various examples.

Video decoder 300 may store the reconstructed blocks in DPB 314. Forinstance, in examples where operations of filter unit 312 are notperformed, reconstruction unit 310 may store reconstructed blocks to DPB314. In examples where operations of filter unit 312 are performed,filter unit 312 may store the filtered reconstructed blocks to DPB 314.As discussed above, DPB 314 may provide reference information, such assamples of a current picture for intra-prediction and previously decodedpictures for subsequent motion compensation, to prediction processingunit 304. Moreover, video decoder 300 may output decoded pictures (e.g.,decoded video) from DPB 314 for subsequent presentation on a displaydevice, such as display device 118 of FIG. 1 .

FIG. 14 is a flowchart illustrating an example method for encoding acurrent block in accordance with the techniques of this disclosure. Thecurrent block may comprise a current CU. Although described with respectto video encoder 200 (FIGS. 1 and 12 ), it should be understood thatother devices may be configured to perform a method similar to that ofFIG. 14 .

In this example, video encoder 200 initially predicts the current block(350). For example, video encoder 200 may form a prediction block forthe current block. Video encoder 200 may then calculate a residual blockfor the current block (352). To calculate the residual block, videoencoder 200 may calculate a difference between the original, uncodedblock and the prediction block for the current block. Video encoder 200may then transform the residual block and quantize transformcoefficients of the residual block (354). Next, video encoder 200 mayscan the quantized transform coefficients of the residual block (356).During the scan, or following the scan, video encoder 200 may entropyencode the transform coefficients (358). For example, video encoder 200may encode the transform coefficients using CAVLC or CABAC. Videoencoder 200 may then output the entropy encoded data of the block (360).

Video encoder 200 may also decode the current block after encoding thecurrent block, to use the decoded version of the current block asreference data for subsequently coded data (e.g., in inter- orintra-prediction modes). Thus, video encoder 200 may inverse quantizeand inverse transform the coefficients to reproduce the residual block(362). Video encoder 200 may combine the residual block with theprediction block to form a decoded block (364). Video encoder 200 mayfurther perform in-loop filtering according to any of the varioustechniques of this disclosure (366), e.g., applying one or more neuralnetwork processing blocks to intermediate filtered video data, each ofthe neural network processing blocks including a first 1×1 convolutionalfilter, a parametric rectified linear unit (PReLU) filter, a second 1×1convolutional filter, and a 3×3 convolutional filter, per FIGS. 8-11 asdiscussed above. Video encoder 200 may then store the filtered, decodedblock in DPB 218 (368). Prior to storing the decoded block, videoencoder 200 may filter the decoded block (or an entire decoded pictureincluding decoded blocks).

FIG. 15 is a flowchart illustrating an example method for decoding acurrent block of video data in accordance with the techniques of thisdisclosure. The current block may comprise a current CU. Althoughdescribed with respect to video decoder 300 (FIGS. 1 and 13 ), it shouldbe understood that other devices may be configured to perform a methodsimilar to that of FIG. 15 .

Video decoder 300 may receive entropy encoded data for the currentblock, such as entropy encoded prediction information and entropyencoded data for transform coefficients of a residual blockcorresponding to the current block (370). Video decoder 300 may entropydecode the entropy encoded data to determine prediction information forthe current block and to reproduce transform coefficients of theresidual block (372). Video decoder 300 may predict the current block(374), e.g., using an intra- or inter-prediction mode as indicated bythe prediction information for the current block, to calculate aprediction block for the current block. Video decoder 300 may theninverse scan the reproduced transform coefficients (376), to create ablock of quantized transform coefficients. Video decoder 300 may theninverse quantize the transform coefficients and apply an inversetransform to the transform coefficients to produce a residual block(378). Video decoder 300 may ultimately decode the current block bycombining the prediction block and the residual block (380). Aftercombining the prediction and residual blocks to form decoded blocks,video decoder 300 may filter the decoded blocks (or an entire decodedpicture including the decoded blocks) (382).

FIG. 16 is a flowchart illustrating an example method for filteringvideo data using a series of one or more residual blocks of a CNN filterin accordance with the techniques of this disclosure. The method may beperformed by video encoder 200 or video decoder 300. The method of FIG.16 is explained with respect to video decoder 300. However, it should beunderstood that other devices, such as video encoder 200, may performthis or a similar method. For example, the method of FIG. 16 maycorrespond to step 366 of FIG. 14 or step 382 of FIG. 15 .

Initially, filter unit 312 of video decoder 300, which may includecomponents similar to those of any of FIGS. 8-11 , filters input datausing respective 3×3 convolutional neural network filters and PReLUfilters (400). The inputs may include, for example, a reconstructedblock of video data, prediction information, a quantization parameter(QP), partition information, and/or deblocking filter information, suchas a boundary strength (BS) value.

Filter unit 312 may then concatenate the filtered inputs (402). Forexample, filter unit 312 may form a three-dimensional set of inputs,where two of the dimensions correspond to the width and height of adecoded picture of video data, and the third dimension corresponds todifferent components, such as red/green/blue or luminance/blue huechrominance/red hue chrominance, as well as the other inputs, e.g.,prediction information, QP, partition information, BS values, or thelike. Each of the other inputs may be provided as a separate componenthaving the size of the picture and sample values corresponding to pixelpositions of the picture, where each of the sample values may have thesame value corresponding to the input for that component. That is, forexample, the QP component may have sample values that are each equal toa corresponding QP.

Filter unit 312 may then apply a fuse filter to the concatenatedfiltered inputs (404). Applying the fuse filter may include applying a1×1 convolutional filter followed by a PReLU filter. Filter unit 312 mayalso apply a transition filter (406), including applying a 3×3convolutional filter followed by a PReLU filter. Filter unit 312 maythen apply a set of one or more residual blocks (or filter blocks)(408). Applying the one or more residual blocks is discussed in greaterdetail below with respect to FIG. 17 .

Filter unit 312 may then apply a 3×3 CNN filter (410), a PReLU filter(412), another 3×3 CNN filter (414), and a pixel shuffle filter (416).Finally, filter unit 312 may combine the resulting filtered data withthe input reconstructed data (418).

FIG. 17 is a flowchart illustrating an example method for executing aresidual block of a CNN filter according to the techniques of thisdisclosure. The method of FIG. 17 may correspond to step 408 of FIG. 16. Each residual block may receive input data (420), apply a 1×1 CNNfilter to the input data (422), apply a PReLU filter (424), apply a 1×1CNN filter (426), apply a 3×3 CNN filter (428), and then combine theoutput of the 3×3 CNN filter with the received input data (430). In someexamples, e.g., as in the case of FIGS. 10 and 11 , the finalcombination step may be omitted to achieve a filter block, as opposed toa residual block.

Various examples of the techniques of this disclosure are summarized inthe following clauses:

Clause 1: A method of filtering video data, the method comprising:applying one or more neural network processing blocks to intermediatefiltered video data, each of the neural network processing blocksincluding a first 1×1 convolutional filter, a parametric rectifiedlinear unit (PReLU) filter, a second 1×1 convolutional filter, and a 3×3convolutional filter; applying additional neural network processingblocks to output of the one or more neural network processing blocks toform filtered video data; and outputting the filtered video data.

Clause 2: The method of clause 1, wherein a number of the one or moreneural network processing blocks is equal to 32.

Clause 3: The method of any of clauses 1 and 2, wherein applying the oneor more neural network processing blocks comprises applying a pluralityof feature maps.

Clause 4: The method of clause 3, wherein a number of the feature mapsis equal to 64.

Clause 5: The method of any of clauses 1-4, wherein applying the one ormore neural network processing blocks comprises combining, by each ofthe neural network processing blocks, input to the neural networkprocessing block with output of the 3×3 convolutional filter as outputfrom the neural network processing block.

Clause 6: The method of any of clauses 1-5, wherein the first 1×1convolutional filter increases a number of channels up to 160, andwherein the PReLU filter reduces the number of channels to 64.

Clause 7: The method of any of clauses 1-6, further comprising formingthe intermediate filtered video data using a fuse neural networkfiltering block and a transition neural network filtering block.

Clause 8: The method of clause 1, wherein a number of the one or moreneural network processing blocks is equal to 32.

Clause 9: The method of clause 1, wherein applying the one or moreneural network processing blocks comprises applying a plurality offeature maps.

Clause 10: The method of clause 9, wherein a number of the feature mapsis equal to 64.

Clause 11: The method of clause 1, wherein applying the one or moreneural network processing blocks comprises combining, by each of theneural network processing blocks, input to the neural network processingblock with output of the 3×3 convolutional filter as output from theneural network processing block.

Clause 12: The method of clause 1, wherein the first 1×1 convolutionalfilter increases a number of channels up to 160, and wherein the PReLUfilter reduces the number of channels to 64.

Clause 13: The method of clause 1, further comprising forming theintermediate filtered video data using a fuse neural network filteringblock and a transition neural network filtering block.

Clause 14: The method of any of clauses 1-13, further comprisingdecoding a current picture of video data and forming the intermediatefiltered video data from the decoded current picture.

Clause 15: The method of clause 14, further comprising encoding thecurrent picture of video data prior to decoding the current picture ofvideo data.

Clause 16: A device for filtering video data, the device comprising oneor more means for performing the method of any of clauses 1-15.

Clause 17: The device of clause 16, wherein the one or more meanscomprise one or more processors implemented in circuitry.

Clause 18: The device of any of clauses 16 and 17, further comprising adisplay configured to display the decoded video data.

Clause 19: The device of any of clauses 16-18, wherein the devicecomprises one or more of a camera, a computer, a mobile device, abroadcast receiver device, or a set-top box.

Clause 20: The device of clause 16-19, further comprising a memoryconfigured to store the video data.

Clause 21: A computer-readable storage medium having stored thereoninstructions that, when executed, cause a processor of a device fordecoding video data to perform the method of any of clauses 1-15.

Clause 22: A device for filtering video data, the device comprising:means for applying one or more neural network processing blocks tointermediate filtered video data, each of the neural network processingblocks including a first 1×1 convolutional filter, a parametricrectified linear unit (PReLU) filter, a second 1×1 convolutional filter,and a 3×3 convolutional filter; means for applying additional neuralnetwork processing blocks to output of the one or more neural networkprocessing blocks to form filtered video data; and means for outputtingthe filtered video data.

Clause 23: A method of filtering video data, the method comprising:applying one or more neural network processing blocks to intermediatefiltered video data, each of the neural network processing blocksincluding a first 1×1 convolutional filter, a parametric rectifiedlinear unit (PReLU) filter, a second 1×1 convolutional filter, and a 3×3convolutional filter; applying additional neural network processingblocks to output of the one or more neural network processing blocks toform filtered video data; and outputting the filtered video data.

Clause 24: The method of clause 23, wherein the one or more neuralnetwork processing blocks comprise residual blocks.

Clause 25: The method of clause 23, wherein a number of the one or moreneural network processing blocks is equal to 32.

Clause 26: The method of clause 23, wherein applying the one or moreneural network processing blocks comprises applying a plurality offeature maps.

Clause 27: The method of clause 26, wherein a number of the feature mapsis equal to 64.

Clause 28: The method of clause 23, wherein applying the one or moreneural network processing blocks comprises combining, by each of theneural network processing blocks, input to the neural network processingblock with output of the 3×3 convolutional filter as output from theneural network processing block.

Clause 29: The method of clause 23, wherein the first 1×1 convolutionalfilter increases a number of channels up to 160, and wherein the PReLUfilter reduces the number of channels to 64.

Clause 30: The method of clause 23, further comprising forming theintermediate filtered video data using a fuse neural network filteringblock and a transition neural network filtering block.

Clause 31: The method of clause 30, wherein the fuse network filteringblock comprises a 1×1 convolutional filter and a second PReLU filter,and wherein the transition neural network filtering block comprises a3×3 convolutional filter and a third PReLU filter.

Clause 32: The method of clause 23, further comprising decoding acurrent picture of video data and forming the intermediate filteredvideo data from the decoded current picture.

Clause 33: The method of clause 32, further comprising encoding thecurrent picture of video data prior to decoding the current picture ofvideo data.

Clause 34: A device for filtering video data, the device comprising: amemory configured to store video data; and a processing systemcomprising one or more processors implemented in circuitry, theprocessing system being configured to: apply one or more neural networkprocessing blocks to intermediate filtered video data, each of theneural network processing blocks including a first 1×1 convolutionalfilter, a parametric rectified linear unit (PReLU) filter, a second 1×1convolutional filter, and a 3×3 convolutional filter; apply additionalneural network processing blocks to output of the one or more neuralnetwork processing blocks to form filtered video data; and output thefiltered video data.

Clause 35: The device of clause 34, wherein the one or more neuralnetwork processing blocks comprise residual blocks.

Clause 36: The device of clause 34, wherein a number of the one or moreneural network processing blocks is equal to 32.

Clause 37: The device of clause 34, wherein to apply the one or moreneural network processing blocks, the processing system is configured toapply a plurality of feature maps.

Clause 38: The device of clause 37, wherein a number of the feature mapsis equal to 64.

Clause 39: The device of clause 34, wherein each of the neural networkprocessing blocks is configured to combine input to the neural networkprocessing block with output of the 3×3 convolutional filter as outputfrom the neural network processing block.

Clause 40: The device of clause 34, wherein the first 1×1 convolutionalfilter is configured to increase a number of channels up to 160, andwherein the PReLU filter is configured to reduce the number of channelsto 64.

Clause 41: The device of clause 34, wherein the processing system isconfigured to form the intermediate filtered video data using a fuseneural network filtering block and a transition neural network filteringblock.

Clause 42: The device of clause 41, wherein the fuse network filteringblock comprises a 1×1 convolutional filter and a second PReLU filter,and wherein the transition neural network filtering block comprises a3×3 convolutional filter and a third PReLU filter.

Clause 43: The device of clause 34, wherein the processing system isfurther configured to decode a current picture of video data and formthe intermediate filtered video data from the decoded current picture.

Clause 44: The device of clause 43, wherein the processing system isfurther configured to encode the current picture of video data beforethe processing system decodes the current picture of video data.

Clause 45: The device of clause 34, further comprising a displayconfigured to display the filtered video data.

Clause 46: The device of clause 34, wherein the device comprises one ormore of a camera, a computer, a mobile device, a broadcast receiverdevice, or a set-top box.

Clause 47: A computer-readable storage medium having stored thereoninstructions that, when executed, cause a processing system to: applyone or more neural network processing blocks to intermediate filteredvideo data, each of the neural network processing blocks including afirst 1×1 convolutional filter, a parametric rectified linear unit(PReLU) filter, a second 1×1 convolutional filter, and a 3×3convolutional filter; apply additional neural network processing blocksto output of the one or more neural network processing blocks to formfiltered video data; and output the filtered video data.

Clause 48: A device for filtering video data, the device comprising:means for applying one or more neural network processing blocks tointermediate filtered video data, each of the neural network processingblocks including a first 1×1 convolutional filter, a parametricrectified linear unit (PReLU) filter, a second 1×1 convolutional filter,and a 3×3 convolutional filter; means for applying additional neuralnetwork processing blocks to output of the one or more neural networkprocessing blocks to form filtered video data; and means for outputtingthe filtered video data.

It is to be recognized that depending on the example, certain acts orevents of any of the techniques described herein can be performed in adifferent sequence, may be added, merged, or left out altogether (e.g.,not all described acts or events are necessary for the practice of thetechniques). Moreover, in certain examples, acts or events may beperformed concurrently, e.g., through multi-threaded processing,interrupt processing, or multiple processors, rather than sequentially.

In one or more examples, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium and executedby a hardware-based processing unit. Computer-readable media may includecomputer-readable storage media, which corresponds to a tangible mediumsuch as data storage media, or communication media including any mediumthat facilitates transfer of a computer program from one place toanother, e.g., according to a communication protocol. In this manner,computer-readable media generally may correspond to (1) tangiblecomputer-readable storage media which is non-transitory or (2) acommunication medium such as a signal or carrier wave. Data storagemedia may be any available media that can be accessed by one or morecomputers or one or more processors to retrieve instructions, codeand/or data structures for implementation of the techniques described inthis disclosure. A computer program product may include acomputer-readable medium.

By way of example, and not limitation, such computer-readable storagemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage, or other magnetic storage devices, flashmemory, or any other medium that can be used to store desired programcode in the form of instructions or data structures and that can beaccessed by a computer. Also, any connection is properly termed acomputer-readable medium. For example, if instructions are transmittedfrom a website, server, or other remote source using a coaxial cable,fiber optic cable, twisted pair, digital subscriber line (DSL), orwireless technologies such as infrared, radio, and microwave, then thecoaxial cable, fiber optic cable, twisted pair, DSL, or wirelesstechnologies such as infrared, radio, and microwave are included in thedefinition of medium. It should be understood, however, thatcomputer-readable storage media and data storage media do not includeconnections, carrier waves, signals, or other transitory media, but areinstead directed to non-transitory, tangible storage media. Disk anddisc, as used herein, includes compact disc (CD), laser disc, opticaldisc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wheredisks usually reproduce data magnetically, while discs reproduce dataoptically with lasers. Combinations of the above should also be includedwithin the scope of computer-readable media.

Instructions may be executed by one or more processors, such as one ormore digital signal processors (DSPs), general purpose microprocessors,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Accordingly, the terms “processor” and “processingcircuitry,” as used herein may refer to any of the foregoing structuresor any other structure suitable for implementation of the techniquesdescribed herein. In addition, in some aspects, the functionalitydescribed herein may be provided within dedicated hardware and/orsoftware modules configured for encoding and decoding, or incorporatedin a combined codec. Also, the techniques could be fully implemented inone or more circuits or logic elements.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including a wireless handset, an integratedcircuit (IC) or a set of ICs (e.g., a chip set). Various components,modules, or units are described in this disclosure to emphasizefunctional aspects of devices configured to perform the disclosedtechniques, but do not necessarily require realization by differenthardware units. Rather, as described above, various units may becombined in a codec hardware unit or provided by a collection ofinteroperative hardware units, including one or more processors asdescribed above, in conjunction with suitable software and/or firmware.

Various examples have been described. These and other examples arewithin the scope of the following claims.

What is claimed is:
 1. A method of filtering video data, the methodcomprising: applying one or more neural network processing blocks tointermediate filtered video data, each of the neural network processingblocks including a first 1×1 convolutional filter, a parametricrectified linear unit (PReLU) filter, a second 1×1 convolutional filter,and a 3×3 convolutional filter; applying additional neural networkprocessing blocks to output of the one or more neural network processingblocks to form filtered video data; and outputting the filtered videodata.
 2. The method of claim 1, wherein the one or more neural networkprocessing blocks comprise residual blocks.
 3. The method of claim 1,wherein a number of the one or more neural network processing blocks isequal to
 32. 4. The method of claim 1, wherein applying the one or moreneural network processing blocks comprises applying a plurality offeature maps.
 5. The method of claim 4, wherein a number of the featuremaps is equal to
 64. 6. The method of claim 1, wherein applying the oneor more neural network processing blocks comprises combining, by each ofthe one or more neural network processing blocks, input to the neuralnetwork processing block with output of the 3×3 convolutional filter asoutput from the neural network processing block.
 7. The method of claim1, wherein the first 1×1 convolutional filter increases a number ofchannels up to 160, and wherein the PReLU filter reduces the number ofchannels to
 64. 8. The method of claim 1, further comprising forming theintermediate filtered video data using a fuse neural network filteringblock and a transition neural network filtering block.
 9. The method ofclaim 8, wherein the fuse network filtering block comprises a 1×1convolutional filter and a second PReLU filter, and wherein thetransition neural network filtering block comprises a 3×3 convolutionalfilter and a third PReLU filter.
 10. The method of claim 1, furthercomprising decoding a current picture of video data and forming theintermediate filtered video data from the decoded current picture. 11.The method of claim 10, further comprising encoding the current pictureof video data prior to decoding the current picture of video data.
 12. Adevice for filtering video data, the device comprising: a memoryconfigured to store video data; and a processing system comprising oneor more processors implemented in circuitry, the processing system beingconfigured to: apply one or more neural network processing blocks tointermediate filtered video data, each of the neural network processingblocks including a first 1×1 convolutional filter, a parametricrectified linear unit (PReLU) filter, a second 1×1 convolutional filter,and a 3×3 convolutional filter; apply additional neural networkprocessing blocks to output of the one or more neural network processingblocks to form filtered video data; and output the filtered video data.13. The device of claim 12, wherein the one or more neural networkprocessing blocks comprise residual blocks.
 14. The device of claim 12,wherein a number of the one or more neural network processing blocks isequal to
 32. 15. The device of claim 12, wherein to apply the one ormore neural network processing blocks, the processing system isconfigured to apply a plurality of feature maps.
 16. The device of claim15, wherein a number of the feature maps is equal to
 64. 17. The deviceof claim 12, wherein each of the one or more neural network processingblocks is configured to combine input to the neural network processingblock with output of the 3×3 convolutional filter as output from theneural network processing block.
 18. The device of claim 12, wherein thefirst 1×1 convolutional filter is configured to increase a number ofchannels up to 160, and wherein the PReLU filter is configured to reducethe number of channels to
 64. 19. The device of claim 12, wherein theprocessing system is configured to form the intermediate filtered videodata using a fuse neural network filtering block and a transition neuralnetwork filtering block.
 20. The device of claim 19, wherein the fusenetwork filtering block comprises a 1×1 convolutional filter and asecond PReLU filter, and wherein the transition neural network filteringblock comprises a 3×3 convolutional filter and a third PReLU filter. 21.The device of claim 12, wherein the processing system is furtherconfigured to decode a current picture of video data and form theintermediate filtered video data from the decoded current picture. 22.The device of claim 21, wherein the processing system is furtherconfigured to encode the current picture of video data before theprocessing system decodes the current picture of video data.
 23. Thedevice of claim 12, further comprising a display configured to displaythe filtered video data.
 24. The device of claim 12, wherein the devicecomprises one or more of a camera, a computer, a mobile device, abroadcast receiver device, or a set-top box.
 25. A computer-readablestorage medium having stored thereon instructions that, when executed,cause a processing system to: apply one or more neural networkprocessing blocks to intermediate filtered video data, each of theneural network processing blocks including a first 1×1 convolutionalfilter, a parametric rectified linear unit (PReLU) filter, a second 1×1convolutional filter, and a 3×3 convolutional filter; apply additionalneural network processing blocks to output of the one or more neuralnetwork processing blocks to form filtered video data; and output thefiltered video data.
 26. A device for filtering video data, the devicecomprising: means for applying one or more neural network processingblocks to intermediate filtered video data, each of the neural networkprocessing blocks including a first 1×1 convolutional filter, aparametric rectified linear unit (PReLU) filter, a second 1×1convolutional filter, and a 3×3 convolutional filter; means for applyingadditional neural network processing blocks to output of the one or moreneural network processing blocks to form filtered video data; and meansfor outputting the filtered video data.